@inproceedings{zimu-etal-2024-exploring,
title = "Exploring Faithful and Informative Commonsense Reasoning and Moral Understanding in Children`s Stories",
author = "Zimu, Wang and
Wang, Yuqi and
Nijia, Han and
Qi, Chen and
Haiyang, Zhang and
Yushan, Pan and
Qiufeng, Wang and
Wei, Wang",
editor = "Lin, Hongfei and
Tan, Hongye and
Li, Bin",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-3.37/",
pages = "327--335",
language = "eng",
abstract = "{\textquotedblleft}Commonsense reasoning and moral understanding are crucial tasks in artificial intelligence (AI) and natural language processing (NLP). However, existing research often falls short in terms of faithfulness and informativeness during the reasoning process. We propose a novel framework for performing commonsense reasoning and moral understanding using large language models (LLMs), involving constructing guided prompts by incorporating relevant knowledge for commonsense reasoning and extracting facts from stories for moral understanding. We conduct extensive experiments on the Commonsense Reasoning and Moral Understanding in Children`s Stories (CRMUS) dataset with widely recognised LLMs under both zero-shot and fine-tuning settings, demonstrating the effectiveness of our proposed method. Furthermore, we analyse the adaptability of different LLMs in extracting facts for moral understanding performance.{\textquotedblright}"
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zimu-etal-2024-exploring">
<titleInfo>
<title>Exploring Faithful and Informative Commonsense Reasoning and Moral Understanding in Children‘s Stories</title>
</titleInfo>
<name type="personal">
<namePart type="given">Wang</namePart>
<namePart type="family">Zimu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yuqi</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Han</namePart>
<namePart type="family">Nijia</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chen</namePart>
<namePart type="family">Qi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhang</namePart>
<namePart type="family">Haiyang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pan</namePart>
<namePart type="family">Yushan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wang</namePart>
<namePart type="family">Qiufeng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wang</namePart>
<namePart type="family">Wei</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<language>
<languageTerm type="text">eng</languageTerm>
</language>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hongfei</namePart>
<namePart type="family">Lin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hongye</namePart>
<namePart type="family">Tan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bin</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Chinese Information Processing Society of China</publisher>
<place>
<placeTerm type="text">Taiyuan, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>“Commonsense reasoning and moral understanding are crucial tasks in artificial intelligence (AI) and natural language processing (NLP). However, existing research often falls short in terms of faithfulness and informativeness during the reasoning process. We propose a novel framework for performing commonsense reasoning and moral understanding using large language models (LLMs), involving constructing guided prompts by incorporating relevant knowledge for commonsense reasoning and extracting facts from stories for moral understanding. We conduct extensive experiments on the Commonsense Reasoning and Moral Understanding in Children‘s Stories (CRMUS) dataset with widely recognised LLMs under both zero-shot and fine-tuning settings, demonstrating the effectiveness of our proposed method. Furthermore, we analyse the adaptability of different LLMs in extracting facts for moral understanding performance.”</abstract>
<identifier type="citekey">zimu-etal-2024-exploring</identifier>
<location>
<url>https://aclanthology.org/2024.ccl-3.37/</url>
</location>
<part>
<date>2024-07</date>
<extent unit="page">
<start>327</start>
<end>335</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Exploring Faithful and Informative Commonsense Reasoning and Moral Understanding in Children‘s Stories
%A Zimu, Wang
%A Wang, Yuqi
%A Nijia, Han
%A Qi, Chen
%A Haiyang, Zhang
%A Yushan, Pan
%A Qiufeng, Wang
%A Wei, Wang
%Y Lin, Hongfei
%Y Tan, Hongye
%Y Li, Bin
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F zimu-etal-2024-exploring
%X “Commonsense reasoning and moral understanding are crucial tasks in artificial intelligence (AI) and natural language processing (NLP). However, existing research often falls short in terms of faithfulness and informativeness during the reasoning process. We propose a novel framework for performing commonsense reasoning and moral understanding using large language models (LLMs), involving constructing guided prompts by incorporating relevant knowledge for commonsense reasoning and extracting facts from stories for moral understanding. We conduct extensive experiments on the Commonsense Reasoning and Moral Understanding in Children‘s Stories (CRMUS) dataset with widely recognised LLMs under both zero-shot and fine-tuning settings, demonstrating the effectiveness of our proposed method. Furthermore, we analyse the adaptability of different LLMs in extracting facts for moral understanding performance.”
%U https://aclanthology.org/2024.ccl-3.37/
%P 327-335
Markdown (Informal)
[Exploring Faithful and Informative Commonsense Reasoning and Moral Understanding in Children’s Stories](https://aclanthology.org/2024.ccl-3.37/) (Zimu et al., CCL 2024)
ACL
- Wang Zimu, Yuqi Wang, Han Nijia, Chen Qi, Zhang Haiyang, Pan Yushan, Wang Qiufeng, and Wang Wei. 2024. Exploring Faithful and Informative Commonsense Reasoning and Moral Understanding in Children’s Stories. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations), pages 327–335, Taiyuan, China. Chinese Information Processing Society of China.